Imaging method for static ct apparatus, static ct apparatus, electronic device, and medium

Abstract

A static CT apparatus and an imaging method for the same are provided. The imaging method includes: acquiring initial projection data of an inspected object at different angles by using a distributed ray source and a detector, where the initial projection data includes projection data that is directly obtained by the detector based on the rays emitted from a plurality of ray source points; obtaining a first CT image using a reconstruction algorithm according to the acquired initial projection data; dividing the first CT image into N first sub-images, where N is a positive integer greater than or equal to 1, and a union of the N first sub-images covers the entire first CT image; optimizing the N first sub-images to obtain N second sub-images; and merging the N second sub-images to obtain a second CT image.

1 . An imaging method for a static CT apparatus, wherein the static CT apparatus comprises a distributed ray source and a detector, the distributed ray source comprises a plurality of ray source points configured to emit rays towards an inspected object, and the detector comprises a plurality of detector units configured to detect the rays passing through the inspected object,
 wherein the imaging method comprises:   an initial projection data acquisition step of acquiring initial projection data of the inspected object at different angles by using the distributed ray source and the detector, wherein the initial projection data comprises projection data that is directly obtained by the detector based on the rays emitted from the plurality of ray source points;   a first image reconstruction step of obtaining a first CT image using a reconstruction algorithm according to the acquired initial projection data;   an image segmentation step of dividing the first CT image into N first sub-images, where N is a positive integer greater than or equal to 1, and a union of the N first sub-images covers the entire first CT image;   a first optimization step of optimizing the N first sub-images to obtain N second sub-images; and   an image merging step of merging the N second sub-images to obtain a second CT image.   
     
     
         2 . The imaging method according to  claim 1 , wherein the plurality of ray source points comprise a first-type ray source point and a second-type ray source point, and the first-type ray source point is at least one of the plurality of ray source points;
 wherein the initial projection data comprises first projection data and second projection data, the first projection data is projection data directly obtained based on a ray emitted from the first-type ray source point, and the second projection data is projection data directly obtained based on a ray emitted from the second-type ray source point; and   wherein the N first sub-images are optimized with the first projection data as an optimization objective in a process of optimizing the N first sub-images.   
     
     
         3 . The imaging method according to  claim 2 , wherein a frequency of the ray emitted from the first-type ray source point is higher than a frequency of the ray emitted from the second-type ray source point. 
     
     
         4 . The imaging method according to  claim 2 , wherein in the first optimization step, the N first sub-images are optimized using a first neural network model to obtain the N second sub-images, and the first neural network model is pre-trained. 
     
     
         5 . The imaging method according to  claim 4 , wherein the first neural network model is pre-trained by:
 performing a forward projection on each second sub-image according to the first-type ray source point so as to obtain first forward projection data;   determining a difference between the first forward projection data and the first projection data; and   adjusting a parameter of the first neural network model according to the difference between the first forward projection data and the first projection data, so as to minimize the difference between the first forward projection data and the first projection data.   
     
     
         6 . The imaging method according to  claim 2 , wherein the plurality of ray source points comprises K first-type ray source points, where K is a positive integer greater than or equal to 1; and
 wherein the initial projection data comprises K first projection data R j , the first projection data R j  is projection data directly obtained based on a ray emitted from a j th  first-type ray source point, j is a positive integer, and 1≤j≤K.   
     
     
         7 . The imaging method according to  claim 6 , wherein the dividing the first CT image into N first sub-images comprises:
 performing a forward projection on the first CT image Q according to the K first-type ray source points so as to obtain K first initial projection data P j , wherein the first initial projection data P j  is projection data obtained by performing a forward projection on the first CT image Q according to the j th  first-type ray source point; and   performing a forward projection on each first sub-image Q i  according to the K first-type ray source points so as to obtain K first projection sub-data P i,j , wherein the first projection sub-data P i,j  is projection data obtained by performing a forward projection on an i th  first sub-image Q i  according to the j th  first-type ray source point, i is a positive integer, and 1≤i≤N,   wherein in a process of dividing the first CT image into N first sub-images, for any first sub-image and any first-type ray source point, the first initial projection data P j  and the first projection sub-data P i,j  are consistent in a partial region U i,j .   
     
     
         8 . The imaging method according to  claim 1 , wherein the acquiring initial projection data of the inspected object at different angles by using the distributed ray source and the detector comprises:
 acquiring the initial projection data of the inspected object in a predetermined scanning angle range by using the distributed ray source and the detector.   
     
     
         9 . The imaging method according to  claim 8 , further comprising:
 a forward projection step of performing a forward projection on the second CT image to obtain second forward projection data; and   a second optimization step of processing the second forward projection data to obtain optimized projection data.   
     
     
         10 . The imaging method according to  claim 9 , wherein in the second optimization step, the second forward projection data is processed using a second neural network model to obtain the optimized projection data, and the second neural network model is pre-trained. 
     
     
         11 . The imaging method according to  claim 9 , wherein the second forward projection data comprises forward projection data obtained by directly performing a forward projection on the second CT image. 
     
     
         12 . The imaging method according to  claim 9 , further comprising:
 a second image reconstruction step of obtaining a third CT image using a reconstruction algorithm based on the optimized projection data.   
     
     
         13 . The imaging method according to  claim 12 , further comprising:
 determining the obtained third CT image as the first CT image; and   iteratively executing the image segmentation step, the first optimization step, the image merging step, the forward projection step, the second optimization step and the second image reconstruction step until an iteration termination condition is met, and determining the third CT image obtained by a last execution of the second image reconstruction step as a final CT image.   
     
     
         14 . The imaging method according to  claim 13 , wherein the iteration termination condition comprises:
 a number of iterations reaching a specified number of iterations; or   a difference between third CT images obtained in two adjacent iterations being less than a specified threshold.   
     
     
         15 . A static CT apparatus, comprising:
 a distributed ray source, wherein distributed ray source comprises a plurality of ray source points configured to emit rays towards an inspected object;   a detector, wherein the detector comprises a plurality of detector units configured to detect the rays passing through the inspected object; and   an imaging device configured to perform:   an initial projection data acquisition step of acquiring initial projection data of the inspected object at different angles by using the distributed ray source and the detector, wherein the initial projection data comprises projection data that is directly obtained by the detector based on the rays emitted from the plurality of ray source points;   a first image reconstruction step of obtaining a first CT image using a reconstruction algorithm according to the acquired initial projection data;   an image segmentation step of dividing the first CT image into N first sub-images, where N is a positive integer greater than or equal to 1, and a union of the N first sub-images covers the entire first CT image;   a first optimization step of optimizing the N first sub-images to obtain N second sub-images; and   an image merging step of merging the N second sub-images to obtain a second CT image.   
     
     
         16 . The apparatus according to  claim 15 , wherein the plurality of ray source points comprise a first-type ray source point and a second-type ray source point, and the first-type ray source point is at least one of the plurality of ray source points;
 wherein the initial projection data comprises first projection data and second projection data, the first projection data is projection data directly obtained based on a ray emitted from the first-type ray source point, and the second projection data is projection data directly obtained based on a ray emitted from the second-type ray source point; and   wherein the imaging device is configured to: optimize the N first sub-images with the first projection data as an optimization objective in a process of optimizing the N first sub-images.   
     
     
         17 . The apparatus according to  claim 16 , wherein a frequency of the ray emitted from the first-type ray source point is higher than a frequency of the ray emitted from the second-type ray source point,
 wherein in the first optimization step, the N first sub-image are optimized using a first neural network model to obtain the N second sub-images, and the first neural network model is pre-trained,   wherein the first neural network model is pre-trained by:   performing a forward projection on each second sub-image according to the first-type ray source point so as to obtain first forward projection data;   determining a difference between the first forward projection data and the first projection data; and   adjusting a parameter of the first neural network model according to the difference between the first forward projection data and the first projection data, so as to minimize the difference between the first forward projection data and the first projection data.   
     
     
         18 . The apparatus according to  claim 16 , wherein in the first optimization step, the N first sub-images are optimized using a first neural network model to obtain the N second sub-images, and the first neural network model is pre-trained. 
     
     
         19 . The apparatus according to  claim 18 , wherein the first neural network model is pre-trained by:
 performing a forward projection on each second sub-image according to the first-type ray source point so as to obtain first forward projection data;   determining a difference between the first forward projection data and the first projection data; and   adjusting a parameter of the first neural network model according to the difference between the first forward projection data and the first projection data, so as to minimize the difference between the first forward projection data and the first projection data.   
     
     
         20 . The apparatus according to  claim 16 , wherein the plurality of ray source points comprises K first-type ray source points, where K is a positive integer greater than or equal to 1; and
 wherein the initial projection data comprises K first projection data R j , the first projection data R j  is projection data directly obtained based on a ray emitted from a j th  first-type ray source point, j is a positive integer, and 1≤j≤K,   wherein the dividing the first CT image into the N first sub-images comprises:   performing a forward projection on the first CT image Q according to K first-type ray source points so as to obtain K first initial projection data P j , wherein the first initial projection data P j  is projection data obtained by performing a forward projection on the first CT image Q according to the j th  first-type ray source point; and   performing a forward projection on each first sub-image Q i  according to the K first-type ray source points so as to obtain K first projection sub-data P i,j , wherein the first projection sub-data P i,j  is projection data obtained by performing a forward projection on an i th  first sub-image Q i  according to the j th  first-type ray source point, i is a positive integer, and 1≤i≤N,   wherein in a process of dividing the first CT image into N first sub-images, for any first sub-image and any first-type ray source point, the first initial projection data P and the first projection sub-data P i,j  are consistent in a partial region U i,j ,   wherein the acquiring initial projection data of the inspected object at different angles by using the distributed ray source and the detector comprises:   acquiring the initial projection data of the inspected object in a predetermined scanning angle range by using the distributed ray source and the detector.   
     
     
         21 . The apparatus according to  claim 20 , wherein the dividing the first CT image into N first sub-images comprises:
 performing a forward projection on the first CT image Q according to the K first-type ray source points so as to obtain K first initial projection data P j , wherein the first initial projection data P j  is projection data obtained by performing a forward projection on the first CT image Q according to the j th  first-type ray source point; and   performing a forward projection on each first sub-image Q i  according to the K first-type ray source points so as to obtain K first projection sub-data P i,j , wherein the first projection sub-data P i,j  is projection data obtained by performing a forward projection on an i th  first sub-image Q i  according to the j th  first-type ray source point, i is a positive integer, and 1≤i≤N,   wherein in a process of dividing the first CT image into N first sub-images, for any first sub-image and any first-type ray source point, the first initial projection data P j  and the first projection sub-data P i,j  are consistent in a partial region U i,j .   
     
     
         22 . The apparatus according to any one of  claim 15 , wherein the acquiring initial projection data of the inspected object at different angles by using the distributed ray source and the detector comprises:
 acquiring the initial projection data of the inspected object in a predetermined scanning angle range by using the distributed ray source and the detector.   
     
     
         23 . The apparatus according to  claim 20 , wherein the imaging device is further configured to perform:
 a forward projection step of performing a forward projection on the second CT image to obtain second forward projection data; and   a second optimization step of processing the second forward projection data to obtain optimized projection data,   wherein in the second optimization step, the second forward projection data is processed using a second neural network model to obtain the optimized projection data, and the second neural network model is pre-trained,   wherein the second forward projection data comprises forward projection data obtained by directly performing a forward projection on the second CT image.   
     
     
         24 . The apparatus according to  claim 23 , wherein in the second optimization step, the second forward projection data is processed using a second neural network model to obtain the optimized projection data, and the second neural network model is pre-trained. 
     
     
         25 . The apparatus according to  claim 23 , wherein the second forward projection data comprises forward projection data obtained by directly performing a forward projection on the second CT image. 
     
     
         26 . The apparatus according to any one of  claim 23 , wherein the imaging device is further configured to perform:
 a second image reconstruction step of obtaining a third CT image using a reconstruction algorithm based on the optimized projection data,   wherein the imaging device is further configured to:   determine the obtained third CT image as the first CT image; and   iteratively execute the image segmentation step, the first optimization step, the image merging step, the forward projection step, the second optimization step and the second image reconstruction step until an iteration termination condition is met, and determine the third CT image obtained by a last execution of the second image reconstruction step as a final CT image,   where the iteration termination condition comprises:   a number of iterations reaching a specified number of iterations; or   a difference between third CT images obtained in two adjacent iterations being less than a specified threshold.   
     
     
         27 . The apparatus according to  claim 26 , wherein the imaging device is further configured to:
 determine the obtained third CT image as the first CT image; and   iteratively execute the image segmentation step, the first optimization step, the image merging step, the forward projection step, the second optimization step and the second image reconstruction step until an iteration termination condition is met, and determine the third CT image obtained by a last execution of the second image reconstruction step as a final CT image.   
     
     
         28 . The apparatus according to  claim 27 , wherein the iteration termination condition comprises:
 a number of iterations reaching a specified number of iterations; or   a difference between third CT images obtained in two adjacent iterations being less than a specified threshold.